The performance of a satellite-based communication system may rely on the ability to precisely position and point the associated communication satellites. The current attitude of a communication satellite or other spacecraft may be determined from onboard inertial sensors, such as gyroscopes, that measure rotational rates of the spacecraft. A flight computer or other guidance control system may maintain the current attitude of the spacecraft by integrating these rotational rates. However, small errors in the measured rotational rates may cause the attitude to “drift,” i.e. propagate into larger and larger errors in attitude measurement over time. To correct these errors, the flight computer may utilize attitude measurements from additional onboard attitude positional sensors (“APS”), such as star field trackers, terrestrial RF beacons, horizon sensors, and the like, that may periodically provide a measurement of the spacecraft's current position.
In order to combine the attitude measurements from the inertial sensors and those from the secondary APS, the flight computer may utilize a conventional Kalman filter, such as an 8-state Kalman filter. A Kalman filter uses a system's dynamics model (such as the physical laws of motion of a satellite), known control inputs to that system, and measurements (such as those from the inertial sensors or APS) to form an estimate of the system's varying quantities (its state) that is better than the estimate obtained by using any one measurement alone. However, the calculations involving the 8×8 matrices in the 8-state Kalman filter may be computationally intensive, requiring more processing power than may be available on older generation computers, such as those tested and qualified flight-ready for satellites and other spacecraft. In addition, if the attitude measurements from the secondary APS are irregular and/or infrequent, the computationally intensive Kalman filter calculations may be executed multiple times without new information from which to better correct errors in the primary inertial sensor measurements, thus wasting processor throughput in the flight computer.
It is with respect to these considerations and others that the disclosure made herein is presented.